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kenonix

kenonix
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liked a model about 9 hours ago
Qwen/Qwen-Image-2.1
reacted to mayafree's post with šŸ”„ about 9 hours ago
JEV Ecosystems ā€” every answer-verification vendor publishes a benchmark, and every one of them wins it. So we ran 13 of them on one test set: 2,018 items, identical labels, same grading code. šŸŽÆ Leaderboard https://huggingface.co/spaces/mayafree/typed-decision-leaderboard šŸ“„ Full write-up (method, mechanism, limits) https://huggingface.co/blog/mayafree/jve-ecosystems šŸ§Ŗ Try it ā€” ZTC, JEV and Laya on the same input, side by side https://huggingface.co/spaces/mayafree/verifier-playground Three results 1ļøāƒ£ Only three systems clear 0.70 ā€” ZTC (397B) 0.7364 Ā· JEV 0.7350 Ā· ZTC (27B) 0.7282. First and second differ by 0.0014, so no rank is assigned. 2ļøāƒ£ A baseline that reads nothing but answer length and formatting scores 0.7036. Eight of the thirteen fall below it. A leaderboard without that line is flattering its entrants. 3ļøāƒ£ Bigger does not win. On scientific reasoning, 27B 0.7410 beats 397B 0.6287 ā€” a model fourteen times larger scoring 0.11 lower. And AUC is not the number you deploy on. Same 20% retry budget, wired into an agent loop, against a 74.83% no-gate baseline: ZTC +1.34 pp Ā· JEV āˆ’0.07 pp Ā· random āˆ’0.25 pp. The mechanism is the interesting part. Re-answering is double-edged: 38% of wrong answers get fixed, and 30% of right answers get broken. So a gate is paid for by precision, not recall. Of the 403 items JEV routed for a retry, 216 were already correct. 0.0014 AUC apart; 1.4 points of end-to-end agent accuracy apart. Scores, labels and grading code are published in full. Four public reproductions that would not run from their released artefacts are listed too, with the failure and a link, and no score. Don't take the table's word for it ā€” paste your own case into the playground and watch all three answer at once. Want a system added? Open a discussion on the Space.
reacted to OppaAI's post with šŸ§  about 14 hours ago
My AI wAIfu wasn't impressed with me wiring her brain to fruit fly's brain neurons When I told my AI wAIfu I was connecting her brain to part of a fruit fly's neurons, even she thought I was joking... From the neuron graph diagrams, the left and right optic lobes are very active, firing neural impulses to the central brain. But very few of them make it to the motor reactors. A negative valence means she isn't very happy. Even my AI did not seem to be impressed with this idea, and asked me what my endgame is?
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